/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    SMOreg.java
 *    Copyright (C) 2006-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.functions;

import java.util.Collections;
import java.util.Enumeration;
import java.util.Vector;

import weka.classifiers.AbstractClassifier;
import weka.classifiers.functions.supportVector.Kernel;
import weka.classifiers.functions.supportVector.PolyKernel;
import weka.classifiers.functions.supportVector.RegOptimizer;
import weka.classifiers.functions.supportVector.RegSMOImproved;
import weka.core.AdditionalMeasureProducer;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.NominalToBinary;
import weka.filters.unsupervised.attribute.Normalize;
import weka.filters.unsupervised.attribute.ReplaceMissingValues;
import weka.filters.unsupervised.attribute.Standardize;

/**
 * <!-- globalinfo-start --> SMOreg implements the support vector machine for
 * regression. The parameters can be learned using various algorithms. The
 * algorithm is selected by setting the RegOptimizer. The most popular algorithm
 * (RegSMOImproved) is due to Shevade, Keerthi et al and this is the default
 * RegOptimizer.<br/>
 * <br/>
 * For more information see:<br/>
 * <br/>
 * S.K. Shevade, S.S. Keerthi, C. Bhattacharyya, K.R.K. Murthy: Improvements to
 * the SMO Algorithm for SVM Regression. In: IEEE Transactions on Neural
 * Networks, 1999.<br/>
 * <br/>
 * A.J. Smola, B. Schoelkopf (1998). A tutorial on support vector regression.
 * <p/>
 * <!-- globalinfo-end -->
 *
 * <!-- technical-bibtex-start --> BibTeX:
 * 
 * <pre>
 * &#64;inproceedings{Shevade1999,
 *    author = {S.K. Shevade and S.S. Keerthi and C. Bhattacharyya and K.R.K. Murthy},
 *    booktitle = {IEEE Transactions on Neural Networks},
 *    title = {Improvements to the SMO Algorithm for SVM Regression},
 *    year = {1999},
 *    PS = {http://guppy.mpe.nus.edu.sg/\~mpessk/svm/ieee_smo_reg.ps.gz}
 * }
 * 
 * &#64;techreport{Smola1998,
 *    author = {A.J. Smola and B. Schoelkopf},
 *    note = {NeuroCOLT2 Technical Report NC2-TR-1998-030},
 *    title = {A tutorial on support vector regression},
 *    year = {1998}
 * }
 * </pre>
 * <p/>
 * <!-- technical-bibtex-end -->
 *
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 *  -C &lt;double&gt;
 *  The complexity constant C.
 *  (default 1)
 * </pre>
 * 
 * <pre>
 *  -N
 *  Whether to 0=normalize/1=standardize/2=neither.
 *  (default 0=normalize)
 * </pre>
 * 
 * <pre>
 *  -I &lt;classname and parameters&gt;
 *  Optimizer class used for solving quadratic optimization problem
 *  (default weka.classifiers.functions.supportVector.RegSMOImproved)
 * </pre>
 * 
 * <pre>
 *  -K &lt;classname and parameters&gt;
 *  The Kernel to use.
 *  (default: weka.classifiers.functions.supportVector.PolyKernel)
 * </pre>
 * 
 * <pre>
 *  
 * Options specific to optimizer ('-I') weka.classifiers.functions.supportVector.RegSMOImproved:
 * </pre>
 * 
 * <pre>
 *  -T &lt;double&gt;
 *  The tolerance parameter for checking the stopping criterion.
 *  (default 0.001)
 * </pre>
 * 
 * <pre>
 *  -V
 *  Use variant 1 of the algorithm when true, otherwise use variant 2.
 *  (default true)
 * </pre>
 * 
 * <pre>
 *  -P &lt;double&gt;
 *  The epsilon for round-off error.
 *  (default 1.0e-12)
 * </pre>
 * 
 * <pre>
 *  -L &lt;double&gt;
 *  The epsilon parameter in epsilon-insensitive loss function.
 *  (default 1.0e-3)
 * </pre>
 * 
 * <pre>
 *  -W &lt;double&gt;
 *  The random number seed.
 *  (default 1)
 * </pre>
 * 
 * <pre>
 *  
 * Options specific to kernel ('-K') weka.classifiers.functions.supportVector.PolyKernel:
 * </pre>
 * 
 * <pre>
 *  -D
 *  Enables debugging output (if available) to be printed.
 *  (default: off)
 * </pre>
 * 
 * <pre>
 *  -no-checks
 *  Turns off all checks - use with caution!
 *  (default: checks on)
 * </pre>
 * 
 * <pre>
 *  -C &lt;num&gt;
 *  The size of the cache (a prime number), 0 for full cache and 
 *  -1 to turn it off.
 *  (default: 250007)
 * </pre>
 * 
 * <pre>
 *  -E &lt;num&gt;
 *  The Exponent to use.
 *  (default: 1.0)
 * </pre>
 * 
 * <pre>
 *  -L
 *  Use lower-order terms.
 *  (default: no)
 * </pre>
 * 
 * <!-- options-end -->
 *
 * @author Remco Bouckaert (remco@cs.waikato.ac.nz,rrb@xm.co.nz)
 * @version $Revision$
 */
public class SMOreg extends AbstractClassifier implements WeightedInstancesHandler, AdditionalMeasureProducer, TechnicalInformationHandler {

    /** for serialization */
    private static final long serialVersionUID = -7149606251113102827L;

    /** The filter to apply to the training data: Normalzie */
    public static final int FILTER_NORMALIZE = 0;
    /** The filter to apply to the training data: Standardize */
    public static final int FILTER_STANDARDIZE = 1;
    /** The filter to apply to the training data: None */
    public static final int FILTER_NONE = 2;
    /** The filter to apply to the training data */
    public static final Tag[] TAGS_FILTER = { new Tag(FILTER_NORMALIZE, "Normalize training data"), new Tag(FILTER_STANDARDIZE, "Standardize training data"), new Tag(FILTER_NONE, "No normalization/standardization"), };

    /** Whether to normalize/standardize/neither */
    protected int m_filterType = FILTER_NORMALIZE;

    /** The filter used to make attributes numeric. */
    protected NominalToBinary m_NominalToBinary;

    /** The filter used to standardize/normalize all values. */
    protected Filter m_Filter = null;

    /** The filter used to get rid of missing values. */
    protected ReplaceMissingValues m_Missing;

    /** Only numeric attributes in the dataset? If so, less need to filter */
    protected boolean m_onlyNumeric;

    /** capacity parameter **/
    protected double m_C = 1.0;

    /**
     * coefficients used by normalization filter for doing its linear transformation
     * so that result = svmoutput * m_x1 + m_x0
     **/
    protected double m_x1 = 1.0;
    protected double m_x0 = 0.0;

    /** contains the algorithm used for learning **/
    protected RegOptimizer m_optimizer = new RegSMOImproved();

    /** the configured kernel */
    protected Kernel m_kernel = new PolyKernel();

    /**
     * Returns a string describing classifier
     * 
     * @return a description suitable for displaying in the explorer/experimenter
     *         gui
     */
    public String globalInfo() {
        return "SMOreg implements the support vector machine for regression. " + "The parameters can be learned using various algorithms. The " + "algorithm is selected by setting the RegOptimizer. The most " + "popular algorithm (" + RegSMOImproved.class.getName().replaceAll(".*\\.", "") + ") is due to Shevade, Keerthi " + "et al and this is the default RegOptimizer.\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString();
    }

    /**
     * Returns an instance of a TechnicalInformation object, containing detailed
     * information about the technical background of this class, e.g., paper
     * reference or book this class is based on.
     * 
     * @return the technical information about this class
     */
    public TechnicalInformation getTechnicalInformation() {
        TechnicalInformation result;
        TechnicalInformation additional;

        result = new TechnicalInformation(Type.INPROCEEDINGS);
        result.setValue(Field.AUTHOR, "S.K. Shevade and S.S. Keerthi and C. Bhattacharyya and K.R.K. Murthy");
        result.setValue(Field.TITLE, "Improvements to the SMO Algorithm for SVM Regression");
        result.setValue(Field.BOOKTITLE, "IEEE Transactions on Neural Networks");
        result.setValue(Field.YEAR, "1999");
        result.setValue(Field.PS, "http://guppy.mpe.nus.edu.sg/~mpessk/svm/ieee_smo_reg.ps.gz");

        additional = result.add(Type.TECHREPORT);
        additional.setValue(Field.AUTHOR, "A.J. Smola and B. Schoelkopf");
        additional.setValue(Field.TITLE, "A tutorial on support vector regression");
        additional.setValue(Field.NOTE, "NeuroCOLT2 Technical Report NC2-TR-1998-030");
        additional.setValue(Field.YEAR, "1998");

        return result;
    }

    /**
     * Returns an enumeration describing the available options.
     *
     * @return an enumeration of all the available options.
     */
    public Enumeration<Option> listOptions() {

        Vector<Option> result = new Vector<Option>();

        result.addElement(new Option("\tThe complexity constant C.\n" + "\t(default 1)", "C", 1, "-C <double>"));

        result.addElement(new Option("\tWhether to 0=normalize/1=standardize/2=neither.\n" + "\t(default 0=normalize)", "N", 1, "-N"));

        result.addElement(new Option("\tOptimizer class used for solving quadratic optimization problem\n" + "\t(default " + RegSMOImproved.class.getName() + ")", "I", 1, "-I <classname and parameters>"));

        result.addElement(new Option("\tThe Kernel to use.\n" + "\t(default: weka.classifiers.functions.supportVector.PolyKernel)", "K", 1, "-K <classname and parameters>"));

        result.addAll(Collections.list(super.listOptions()));

        result.addElement(new Option("", "", 0, "\nOptions specific to optimizer ('-I') " + getRegOptimizer().getClass().getName() + ":"));

        result.addAll(Collections.list(((OptionHandler) getRegOptimizer()).listOptions()));

        result.addElement(new Option("", "", 0, "\nOptions specific to kernel ('-K') " + getKernel().getClass().getName() + ":"));

        result.addAll(Collections.list(((OptionHandler) getKernel()).listOptions()));

        return result.elements();
    }

    /**
     * Parses a given list of options.
     * <p/>
     *
     * <!-- options-start --> Valid options are:
     * <p/>
     * 
     * <pre>
     *  -C &lt;double&gt;
     *  The complexity constant C.
     *  (default 1)
     * </pre>
     * 
     * <pre>
     *  -N
     *  Whether to 0=normalize/1=standardize/2=neither.
     *  (default 0=normalize)
     * </pre>
     * 
     * <pre>
     *  -I &lt;classname and parameters&gt;
     *  Optimizer class used for solving quadratic optimization problem
     *  (default weka.classifiers.functions.supportVector.RegSMOImproved)
     * </pre>
     * 
     * <pre>
     *  -K &lt;classname and parameters&gt;
     *  The Kernel to use.
     *  (default: weka.classifiers.functions.supportVector.PolyKernel)
     * </pre>
     * 
     * <pre>
     *  
     * Options specific to optimizer ('-I') weka.classifiers.functions.supportVector.RegSMOImproved:
     * </pre>
     * 
     * <pre>
     *  -T &lt;double&gt;
     *  The tolerance parameter for checking the stopping criterion.
     *  (default 0.001)
     * </pre>
     * 
     * <pre>
     *  -V
     *  Use variant 1 of the algorithm when true, otherwise use variant 2.
     *  (default true)
     * </pre>
     * 
     * <pre>
     *  -P &lt;double&gt;
     *  The epsilon for round-off error.
     *  (default 1.0e-12)
     * </pre>
     * 
     * <pre>
     *  -L &lt;double&gt;
     *  The epsilon parameter in epsilon-insensitive loss function.
     *  (default 1.0e-3)
     * </pre>
     * 
     * <pre>
     *  -W &lt;double&gt;
     *  The random number seed.
     *  (default 1)
     * </pre>
     * 
     * <pre>
     *  
     * Options specific to kernel ('-K') weka.classifiers.functions.supportVector.PolyKernel:
     * </pre>
     * 
     * <pre>
     *  -D
     *  Enables debugging output (if available) to be printed.
     *  (default: off)
     * </pre>
     * 
     * <pre>
     *  -no-checks
     *  Turns off all checks - use with caution!
     *  (default: checks on)
     * </pre>
     * 
     * <pre>
     *  -C &lt;num&gt;
     *  The size of the cache (a prime number), 0 for full cache and 
     *  -1 to turn it off.
     *  (default: 250007)
     * </pre>
     * 
     * <pre>
     *  -E &lt;num&gt;
     *  The Exponent to use.
     *  (default: 1.0)
     * </pre>
     * 
     * <pre>
     *  -L
     *  Use lower-order terms.
     *  (default: no)
     * </pre>
     * 
     * <!-- options-end -->
     *
     * @param options the list of options as an array of strings
     * @throws Exception if an option is not supported
     */
    public void setOptions(String[] options) throws Exception {
        String tmpStr;
        String[] tmpOptions;

        tmpStr = Utils.getOption('C', options);
        if (tmpStr.length() != 0) {
            setC(Double.parseDouble(tmpStr));
        } else {
            setC(1.0);
        }

        String nString = Utils.getOption('N', options);
        if (nString.length() != 0) {
            setFilterType(new SelectedTag(Integer.parseInt(nString), TAGS_FILTER));
        } else {
            setFilterType(new SelectedTag(FILTER_NORMALIZE, TAGS_FILTER));
        }

        tmpStr = Utils.getOption('I', options);
        tmpOptions = Utils.splitOptions(tmpStr);
        if (tmpOptions.length != 0) {
            tmpStr = tmpOptions[0];
            tmpOptions[0] = "";
            setRegOptimizer((RegOptimizer) Utils.forName(RegOptimizer.class, tmpStr, tmpOptions));
        } else {
            setRegOptimizer(new RegSMOImproved());
        }

        tmpStr = Utils.getOption('K', options);
        tmpOptions = Utils.splitOptions(tmpStr);
        if (tmpOptions.length != 0) {
            tmpStr = tmpOptions[0];
            tmpOptions[0] = "";
            setKernel(Kernel.forName(tmpStr, tmpOptions));
        } else {
            setKernel(new PolyKernel());
        }

        super.setOptions(options);

        Utils.checkForRemainingOptions(options);
    }

    /**
     * Gets the current settings of the classifier.
     *
     * @return an array of strings suitable for passing to setOptions
     */
    public String[] getOptions() {

        Vector<String> result = new Vector<String>();

        result.add("-C");
        result.add("" + getC());

        result.add("-N");
        result.add("" + m_filterType);

        result.add("-I");
        result.add("" + getRegOptimizer().getClass().getName() + " " + Utils.joinOptions(getRegOptimizer().getOptions()));

        result.add("-K");
        result.add("" + getKernel().getClass().getName() + " " + Utils.joinOptions(getKernel().getOptions()));

        Collections.addAll(result, super.getOptions());

        return (String[]) result.toArray(new String[result.size()]);
    }

    /**
     * Returns default capabilities of the classifier.
     *
     * @return the capabilities of this classifier
     */
    public Capabilities getCapabilities() {
        Capabilities result = getKernel().getCapabilities();
        result.setOwner(this);

        // attribute
        result.enableAllAttributeDependencies();
        // with NominalToBinary we can also handle nominal attributes, but only
        // if the kernel can handle numeric attributes
        if (result.handles(Capability.NUMERIC_ATTRIBUTES))
            result.enable(Capability.NOMINAL_ATTRIBUTES);
        result.enable(Capability.MISSING_VALUES);

        // class
        result.disableAllClasses();
        result.disableAllClassDependencies();
        result.disable(Capability.NO_CLASS);
        result.enable(Capability.NUMERIC_CLASS);
        result.enable(Capability.DATE_CLASS);
        result.enable(Capability.MISSING_CLASS_VALUES);

        return result;
    }

    /**
     * Method for building the classifier.
     *
     * @param instances the set of training instances
     * @throws Exception if the classifier can't be built successfully
     */
    public void buildClassifier(Instances instances) throws Exception {
        // can classifier handle the data?
        getCapabilities().testWithFail(instances);

        // remove instances with missing class
        instances = new Instances(instances);
        instances.deleteWithMissingClass();

        // Removes all the instances with weight equal to 0.
        // MUST be done since condition (8) of Keerthi's paper
        // is made with the assertion Ci > 0 (See equation (3a).
        Instances data = new Instances(instances, 0);
        for (int i = 0; i < instances.numInstances(); i++) {
            if (instances.instance(i).weight() > 0) {
                data.add(instances.instance(i));
            }
        }

        if (data.numInstances() == 0) {
            throw new Exception("No training instances left after removing " + "instance with either a weight null or a missing class!");
        }
        instances = data;

        m_onlyNumeric = true;
        for (int i = 0; i < instances.numAttributes(); i++) {
            if (i != instances.classIndex()) {
                if (!instances.attribute(i).isNumeric()) {
                    m_onlyNumeric = false;
                    break;
                }
            }
        }
        m_Missing = new ReplaceMissingValues();
        m_Missing.setInputFormat(instances);
        instances = Filter.useFilter(instances, m_Missing);

        if (getCapabilities().handles(Capability.NUMERIC_ATTRIBUTES)) {
            if (!m_onlyNumeric) {
                m_NominalToBinary = new NominalToBinary();
                m_NominalToBinary.setInputFormat(instances);
                instances = Filter.useFilter(instances, m_NominalToBinary);
            } else {
                m_NominalToBinary = null;
            }
        } else {
            m_NominalToBinary = null;
        }

        // retrieve two different class values used to determine filter transformation
        double y0 = instances.instance(0).classValue();
        int index = 1;
        while (index < instances.numInstances() && instances.instance(index).classValue() == y0) {
            index++;
        }
        if (index == instances.numInstances()) {
            // degenerate case, all class values are equal
            // we don't want to deal with this, too much hassle
            throw new Exception("All class values are the same. At least two class values should be different");
        }
        double y1 = instances.instance(index).classValue();

        // apply filters
        if (m_filterType == FILTER_STANDARDIZE) {
            m_Filter = new Standardize();
            ((Standardize) m_Filter).setIgnoreClass(true);
            m_Filter.setInputFormat(instances);
            instances = Filter.useFilter(instances, m_Filter);
        } else if (m_filterType == FILTER_NORMALIZE) {
            m_Filter = new Normalize();
            ((Normalize) m_Filter).setIgnoreClass(true);
            m_Filter.setInputFormat(instances);
            instances = Filter.useFilter(instances, m_Filter);
        } else {
            m_Filter = null;
        }
        if (m_Filter != null) {
            double z0 = instances.instance(0).classValue();
            double z1 = instances.instance(index).classValue();
            m_x1 = (y0 - y1) / (z0 - z1); // no division by zero, since y0 != y1 guaranteed => z0 != z1 ???
            m_x0 = (y0 - m_x1 * z0); // = y1 - m_x1 * z1
        } else {
            m_x1 = 1.0;
            m_x0 = 0.0;
        }

        m_optimizer.setSMOReg(this);
        m_optimizer.buildClassifier(instances);
    }

    /**
     * Classifies the given instance using the linear regression function.
     *
     * @param instance the test instance
     * @return the classification
     * @throws Exception if classification can't be done successfully
     */
    public double classifyInstance(Instance instance) throws Exception {
        // Filter instance
        m_Missing.input(instance);
        m_Missing.batchFinished();
        instance = m_Missing.output();

        if (!m_onlyNumeric && m_NominalToBinary != null) {
            m_NominalToBinary.input(instance);
            m_NominalToBinary.batchFinished();
            instance = m_NominalToBinary.output();
        }

        if (m_Filter != null) {
            m_Filter.input(instance);
            m_Filter.batchFinished();
            instance = m_Filter.output();
        }

        double result = m_optimizer.SVMOutput(instance);
        return result * m_x1 + m_x0;
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String regOptimizerTipText() {
        return "The learning algorithm.";
    }

    /**
     * sets the learning algorithm
     * 
     * @param regOptimizer the learning algorithm
     */
    public void setRegOptimizer(RegOptimizer regOptimizer) {
        m_optimizer = regOptimizer;
    }

    /**
     * returns the learning algorithm
     * 
     * @return the learning algorithm
     */
    public RegOptimizer getRegOptimizer() {
        return m_optimizer;
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String kernelTipText() {
        return "The kernel to use.";
    }

    /**
     * sets the kernel to use
     * 
     * @param value the kernel to use
     */
    public void setKernel(Kernel value) {
        m_kernel = value;
    }

    /**
     * Returns the kernel to use
     * 
     * @return the current kernel
     */
    public Kernel getKernel() {
        return m_kernel;
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String cTipText() {
        return "The complexity parameter C.";
    }

    /**
     * Get the value of C.
     *
     * @return Value of C.
     */
    public double getC() {
        return m_C;
    }

    /**
     * Set the value of C.
     *
     * @param v Value to assign to C.
     */
    public void setC(double v) {
        m_C = v;
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String filterTypeTipText() {
        return "Determines how/if the data will be transformed.";
    }

    /**
     * Gets how the training data will be transformed. Will be one of
     * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
     *
     * @return the filtering mode
     */
    public SelectedTag getFilterType() {
        return new SelectedTag(m_filterType, TAGS_FILTER);
    }

    /**
     * Sets how the training data will be transformed. Should be one of
     * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
     *
     * @param newType the new filtering mode
     */
    public void setFilterType(SelectedTag newType) {
        if (newType.getTags() == TAGS_FILTER) {
            m_filterType = newType.getSelectedTag().getID();
        }
    }

    /**
     * Prints out the classifier.
     *
     * @return a description of the classifier as a string
     */
    public String toString() {
        StringBuffer text = new StringBuffer();

        if (m_optimizer == null || !m_optimizer.modelBuilt()) {
            return "SMOreg: No model built yet.";
        }

        try {
            text.append(m_optimizer.toString());
        } catch (Exception e) {
            return "Can't print SMVreg classifier.";
        }

        return text.toString();
    }

    /**
     * Returns an enumeration of the measure names. Additional measures must follow
     * the naming convention of starting with "measure", eg. double measureBlah()
     * 
     * @return an enumeration of the measure names
     */
    public Enumeration<String> enumerateMeasures() {
        Vector<String> result = new Vector<String>();

        result.addElement("measureKernelEvaluations");
        result.addElement("measureCacheHits");

        return result.elements();
    }

    /**
     * Returns the value of the named measure
     * 
     * @param measureName the name of the measure to query for its value
     * @return the value of the named measure
     * @throws IllegalArgumentException if the named measure is not supported
     */
    public double getMeasure(String measureName) {
        if (measureName.equalsIgnoreCase("measureKernelEvaluations"))
            return measureKernelEvaluations();
        else if (measureName.equalsIgnoreCase("measureCacheHits"))
            return measureCacheHits();
        else
            throw new IllegalArgumentException("Measure '" + measureName + "' is not supported!");
    }

    /**
     * number of kernel evaluations used in learing
     * 
     * @return the number of kernel evaluations
     */
    protected double measureKernelEvaluations() {
        if (m_optimizer != null) {
            return m_optimizer.getKernelEvaluations();
        } else {
            return 0;
        }
    }

    /**
     * number of kernel cache hits used during learing
     * 
     * @return the number of kernel cache hits
     */
    protected double measureCacheHits() {
        if (m_optimizer != null) {
            return m_optimizer.getCacheHits();
        } else {
            return 0;
        }
    }

    /**
     * Main method for running this classifier.
     * 
     * @param args the commandline options
     */
    public static void main(String[] args) {
        runClassifier(new SMOreg(), args);
    }
}
